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-rw-r--r--example_exersize.py132
1 files changed, 132 insertions, 0 deletions
diff --git a/example_exersize.py b/example_exersize.py
new file mode 100644
index 0000000..803ccac
--- /dev/null
+++ b/example_exersize.py
@@ -0,0 +1,132 @@
+
+from pylab import *
+import pylab
+from scipy import *
+
+def plotfit_gaus(indict,newfig=True,txtp=True):
+ if not indict.has_key('x'):
+ print "forgot to use ret_all on the fittodata?";
+ return;
+ p1 = indict['params'];
+ x = indict['x']
+ y = indict['y']
+ err = indict['err']
+ ffunc = indict['ffunc']
+ pylab.errorbar(x,y,fmt='b.',yerr=err)
+ d=pylab.plot(x,y,'k.')
+ fx = x
+ if pylab.size(fx) < 100:
+ fx = r_[min(x):max(x):(max(x)-min(x))/150.]
+ f=pylab.plot(fx,ffunc(p1,fx),'r-')
+ #fy = indict['ffunc'](indict['params'],indict['x'])
+ pylab.title("Gaussian Fit of Events")
+ pylab.xlabel("Number of Charged Particles")
+ pylab.ylabel("Events")
+ p1 = indict['params']
+ p1_err = indict['param_error']
+
+ ax = pylab.gca()
+# ax.set_xlim([-1,14])
+ extra_txt = 'reduced chi squared: %g'\
+ % (indict['rchisqr'])
+ for i in range(scipy.size(p1)):
+ extra_txt += '\np[%i]: %g +/- %g' \
+ % (i,p1[i],p1_err[i]);
+
+# if(pylab.size(p1) == 2):
+# extra_txt = 'reduced chi squared: %g\np[0]: %g\np[1]: %g' \
+# % (indict['rchisqr'],p1[0],p1[1])
+# else:
+# extra_txt= 'reduced chi squared:%g\np[0]:%g, +/- %g\n p[1]:%g\np[2]:%g' \
+# % (indict['rchisqr'],p1[0],p1[1],p1[2])
+
+ if txtp:
+ t = pylab.text(.77, .75, extra_txt,
+ horizontalalignment='center',
+ verticalalignment='center',
+ transform = ax.transAxes)
+ pylab.legend((d,f),('Data', 'Fit'))
+
+def plotfit_poiss(indict,newfig=True,txtp=True):
+ if not indict.has_key('x'):
+ print "forgot to use ret_all on the fittodata?";
+ return;
+ p1 = indict['params'];
+ x = indict['x']
+ y = indict['y']
+ err = indict['err']
+ ffunc = indict['ffunc']
+# if newfig: pylab.figure();
+ pylab.errorbar(x,y,fmt='b.',yerr=err)
+ d=pylab.plot(x,y,'k.')
+ fx = x
+ if pylab.size(fx) < 100:
+ fx = r_[min(x):max(x):(max(x)-min(x))/150.]
+ p1= p1[0]
+ if txtp:
+ f2=pylab.plot(fx,ffunc(p1,fx),'r-')
+ else:
+ f2=pylab.plot(fx,ffunc(p1,fx),'g-')
+ #fy = indict['ffunc'](indict['params'],indict['x'])
+ pylab.title("Poisson Fit of Events")
+ pylab.xlabel("Number of Charged Particles")
+ pylab.ylabel("Events")
+ p1 = indict['params']
+ p1_err = indict['param_error']
+
+ p1= p1[0]
+ ax = pylab.gca()
+ #ax.set_xlim([-1,20])
+ extra_txt = 'reduced chi squared: %g'\
+ % (indict['rchisqr'])
+ for i in range(scipy.size(p1)):
+ if type(p1_err) is not type(9.2):
+ extra_txt += '\np[%i]: %g +/- %g' \
+ % (i,p1[i],p1_err[i]);
+ else:
+ extra_txt += '\np[%i]: %g' \
+ % (i,p1[i]);
+
+# if(pylab.size(p1) == 2):
+# extra_txt = 'reduced chi squared: %g\np[0]: %g\np[1]: %g' \
+# % (indict['rchisqr'],p1[0],p1[1])
+# else:
+# extra_txt= 'reduced chi squared:%g\np[0]:%g, +/- %g\n p[1]:%g\np[2]:%g' \
+# % (indict['rchisqr'],p1[0],p1[1],p1[2])
+ if txtp:
+ t = pylab.text(.77, .75, extra_txt,
+ horizontalalignment='center',
+ verticalalignment='center',
+ transform = ax.transAxes)
+ if txtp:
+ pylab.legend((d,f2),('Data', 'Fit'))
+
+lsd = pylab.load("lineshapedata.txt")
+ls1 = pylab.load("lineshape1.txt")
+
+rawlsd = []
+for i in range(size(lsd[0,:])):
+ for j in range(lsd[1,i]):
+ rawlsd.append(lsd[0,i])
+
+rawls1 = []
+for i in range(size(ls1[0,:])):
+ for j in range(ls1[1,i]):
+ rawls1.append(ls1[0,i])
+
+plotfit(fittodata_lorentz(lsd[0,:],lsd[1,:],[50.0,20.0],err=sqrt(lsd[1,:]),ret_all=True))
+hist(rawlsd, bins=lsd[0,:],align='center',facecolor="lightblue")
+pylab.title("Lorentzian fit to lineshapedata.txt")
+plotfit(fittodata_gaussian(lsd[0,:],lsd[1,:],[50.0,20.0],err=sqrt(lsd[1,:]),ret_all=True))
+hist(rawlsd, bins=lsd[0,:],align='center',facecolor="lightblue")
+pylab.title("Gaussian fit to lineshapedata.txt")
+
+plotfit(fittodata_lorentz(ls1[0,:],ls1[1,:],[50.0,20.0],err=sqrt(ls1[1,:]),ret_all=True))
+hist(rawls1, bins=ls1[0,:],align='center',facecolor="lightblue")
+pylab.title("Lorentzian fit to lineshape1.txt")
+plotfit(fittodata_gaussian(ls1[0,:],ls1[1,:],[50.0,20.0],err=sqrt(ls1[1,:]),ret_all=True))
+hist(rawls1, bins=ls1[0,:],align='center',facecolor="lightblue")
+pylab.title("Gaussian fit to lineshape1.txt")
+
+#pylab.figure()
+#plot(ls1[0,:],ls1[1,:])